Abstract: As an essential part of power system design and operation scheduling，electric load forecasting is

troubled by strong randomness and low accuracy. Moreover，the application of advanced forecasting algorithms is

associated with data management system，while the traditional data management system is very inconvenient for the transmission and management of the data resources and the application of prediction information. In order to

overcome the above problems，based on the cloud platform，weather forecast and historical electric load information was provided for electric load prediction on the basis of achieving efficient data collection and management. In the process of day-ahead load forecasting，aiming at the insufficient ability of single long short-term memory（LSTM）neural network to mine time series data，the method of wavelet transform（WT）was adopted at the same time，which refines the high-frequency components，and improves the forecasting accuracy of the day-ahead power load with the help of the next day's temperature and relative humidity forecast information. The results show that the proposedWTLSTM method has a good prediction effect，and its two-day root mean square errors are 185.56 and 179.56 respectively，prediction accuracies are 61.48% and 12.51% higher than the simple LSTMneural network.

      Key words: electric load forecasting；cloud platform；long short-term memory（LSTM）neural networks；

wavelet transform（WT）